Air pollution emissions in GHG stabilization scenarios

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3.4 Resulting emissions

3.4.2 Air pollution emissions in GHG stabilization scenarios

In addition to their positive impacts on climate change, greenhouse gas mitigation strategies result in numerous positive side effects in other policy areas, such as reduced environmental pressure or improved energy supply security (Amann et al., 2008d). As discussed in Section 3.1, policies targeted at global GHG stabilization require significant changes in the global energy system. The methodology presented in this report allows quantifying the co-benefits of such changes on emissions of air pollutants. It should be emphasized that the synergies of GHG mitigation emerge solely from the reconfiguration of the energy system, and not from more stringent air pollution emission control measures under a climate protection regime.

The relation between CO2 mitigation and air pollution abatement is depicted in Figure 10, showing the reductions in land-based SO2, NOx and CO emissions relative to the CO2 reductions that emerge from decarbonization targets. Such targets force global GHG concentrations to stay

below 450 ppm CO2 equivalents within the computation period 2000-2100. The figure shows that until 2030 the 30% CO2 reduction compared to the baseline is accompanied with nearly proportional reductions of SO2 and NOx emissions. However, until 2050, these co-benefits decrease in relative terms, as the air pollution reduction potential will be largely exploited already in the CLE cases without climate constraints. In addition, the B2 baseline energy scenario assumes a high share of clean and zero-carbon fuels in the fuel-mix, which leaves only a limited space for further fuel substitution in the mid of the century. Reductions in CO in the climate mitigation case, while being significant, are not as high as for SO2 and NOx due to the high effectiveness of CLE measures within the transport sector and because of continued solid fuel combustion in the households, even if fuel switches are taken into account. Figure 8 also illustrates the range of co-benefits originating from different assumptions on the implementation schedules of pollution controls between the conservative scenario with fixed emission coefficients (B2 CLE 2030) and the more optimistic case assuming faster implementation of abatement measures globally due to growing welfare (B2 CLE GDP).

-60%

-50%

-40%

-30%

-20%

-10%

0%

-40% -30% -20% -10% 0%

Reduction of SO2, NOx and CO over Baseline

Reduction of CO2 over Baseline

SO2 CLE 2030 SO2 CLE GDP NOx CLE 2030 NOx CLE GDP CO CLE 2030

CO CLE GDP 2020

2030

2040

2050 2010

Figure 10: Reduction of global air pollution relative to the CO2 emission reductions in the climate stabilization scenario (B2_450ppm) over the B2 baseline.

4 Projections of future emissions from marine shipping

For developing consistent global long-term emission trajectories it is important that all important emission sources are taken into account. Although the international shipping sector is not explicitly represented in either the GAINS or the MESSAGE modeling frameworks, it is expected to contribute significantly to emissions in the next few decades. This section explains the methodology for calculating future emissions from this sector using the underlying GDP and energy projections of the MESSAGE scenario.

With growing GDP, trade volumes and thus ship movements are expected to substantially increase in the future, which will lead to higher fuel consumption and combustion exhausts from this activity. Emissions from international ships are not subject to national regulations, but are dealt with by agreements under the Marine Pollution Convention (MARPOL) of the International Maritime Organization (IMO, 1998).

The projections of NOx emissions from international ships reported here are based on the methodology described in Eyring et al. (2005) and reflect the implementation of the recent IMO standards (IMO, 2008) under the “current legislation” policy scenario overlaying the B2 reference case. Future fuel consumption by international ships is derived from historical relations between GDP, seaborne trade and the number of ships. Figure 11 shows the time evolution of global GDP in the B2 scenario and the corresponding fuel use in ship engines. An important assumption concerning the future exhausts from ships is related to the expected efficiency improvements and the use of alternative fuels. Three cases are illustrated for efficiency improvement ranging from 0% to 25%. The latter case corresponds most closely to the storyline of the B2 scenario, which implies a significant technological learning and innovation processes.

It is further assumed that all new ships will comply with the IMO standards. Eyring et al. (2005) indicates that the original IMO compliance would reduce in 2050 average NOx emission factors for shipping by 30% relative to present day (IMO old), while the updated IMO standards reduce specific emissions by 70% (IMO new). The actual emission reduction due to the adoption of the IMO regulations itself is a source of uncertainty. For instance, Cofala et al. (2007b) suggests a lower reduction impact due to IMO standards for NOx at around 15%.

To illustrate the combined impact of the assumptions on efficiency improvements and lower emission factors, a set of sensitivity cases is presented in Figure 11 (right panel). In 2100 ship emissions could range between more than 65 Mt NOx (without any emission controls) and 4 Mt when assuming 25% fuel savings and NOx control measures beyond the recent IMO requirements (IMO new+). In the less optimistic scenario (i.e., efficiency improvements of 10%

and the 15% lower emission rates) the increase in global NOx shipping emissions would compensate the emission reductions achieved at the land-based emissions.

0 10 20 30 40

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Shipping fuel use (EJ/yr)

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Shipping NOx emissions (Mt NOx/yr)

0% / 0% (no IMO) 10% / -15%

25% / -30% (IMO old) 25% / -70% (IMO new) 25% / -90% (IMO new+)

Figure 11: Global GDP in the B2 scenario and global fuel consumption for international shipping assuming different level of efficiency improvements (Left panel). Global NOx emissions from international shipping; the first column in the legend refers to the efficiency improvement assumed; the second column refers to the expected decrease in the average emission factor by 2050 (Right panel).

5 Conclusions

In order to quantify co-benefits of GHG abatement for air pollution, it is necessary to combine existing information on short-term emission control legislation in the various world regions with term projections of energy use. This report presents a methodology to link short- and long-term energy scenarios and calculate resulting air pollution emissions in a coherent way. The methodology has been implemented for the GAINS and MESSAGE models developed at IIASA. While this approach enables an outlook into longer term perspectives of air pollution emissions, the usual uncertainties associated with projecting the distant future prevail. These include uncertainties about economic development, population growth, technology dynamics, and the extent and speed of implementation of specific air quality policies.

To illustrate the impact of such uncertainties, the paper presents two cases with different assumptions on future air quality legislation: a) a pessimistic case assuming that technologies and legislation would not change beyond 2030, and b) a more optimistic case where emission standards in all countries continue to improve and converge over time to today’s best available technology. These two cases result in significantly different emission levels, especially for NOx. The difference in the results illustrates clearly the importance of transparent reporting of underlying assumptions for air quality policies in long-term greenhouse-gas emission scenarios.

Similarly, the interpretation of the results requires careful consideration. For instance, air pollutant emissions from scenarios that assume technological improvements in emission factors should not be misinterpreted as autonomous trends in absence of dedicated air pollution policies. This would discount the need for future air quality legislation, while in fact in the past

much of the improvements in air pollution emissions resulted from targeted policy interventions. By the same token, long-term energy scenarios that assume no further technological improvements are likely to overestimate future air pollution emissions, as policy interventions could be quite successful in reducing emission levels, as has been demonstrated in the past.

The report also highlights a few methodological issues. First, the underlying baseline activity projections scenarios developed with the MESSAGE model and the national/regional scenarios implemented in GAINS should be in reasonable agreement, so that the emission factors that serve as model interface are representative for the given scenario. Furthermore, emission characteristics of future technologies have to be assessed carefully. New technologies, many of them not existing at present, are expected to dominate the energy markets in the second half of century and will determine future emission profiles. The levels of emissions reductions and associated costs of the implementation of current legislation will depend strongly on the level of the emissions in the reference scenario, as well as on the choice of the baseline assumptions with respect to technology and structural changes in the energy system.

For the next few decades the trends of SO2, NOx and CO emissions in the global B2 CLE scenarios agree well with the short-term “current legislation” scenarios that rely on national energy projections. However, the new global long-term emission projections, especially for NOx

and CO, are significantly lower than those reported earlier, for example in the SRES/IPCC scenarios, as these earlier scenarios did not foresee the recent air pollution control in many parts of the world.

International shipping will constitute an increasing source of global air pollution emissions. A parametric analysis of NOx emissions from international maritime shipping shows that the benefits of all efforts to reduce land-based emissions could be leveled out by a 2% annual growth in global maritime shipping emissions, unless the recent IMO standards were effectively implemented.

The paper also indicates that the implementation of stringent carbon mitigation strategies will also lead to significant reductions in air pollution emissions due to changes in the fuel mixes and demand reductions. Especially the rapid substitution of coal with low carbon fuels in the power sector will reduce SO2 and NOx emissions as a side effect.

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Appendix I

Query statement routine to calculate abated emission factors from the GAINS database compatible with the energy system of the MESSAGE model (example for NOx emissions).

URL:jdbc:oracle:thin:@seine.iiasa.ac.at:1521:RESRCH2

delete from gains_glob.emiss_tmp

insert into gains_glob.emiss_tmp select r.m_reg, r.idregions, r.country, e.IDPOLLUTANT_FRACTIONS,

e.idyears, e.idact, e.idsec, e.activity, e.emiss from

gains_glob.MESSAGE_REGIONS_ALL r join rains_europe.emiss_all_message e on r.idregions=e.idregions and r.gains_scen=e.idscenarios where r.gains_scheama='rains_europe' and IDPOLLUTANT_FRACTIONS in('NOX') -- rains_europe-->17303

insert into gains_glob.emiss_tmp select r.m_reg, r.idregions, r.country, e.IDPOLLUTANT_FRACTIONS,

e.idyears, e.idact, e.idsec, e.activity, e.emiss from

gains_glob.MESSAGE_REGIONS_ALL r join gains_china.emiss_all_message e on r.idregions=e.idregions and r.gains_scen=e.idscenarios where r.gains_scheama='gains_china' and IDPOLLUTANT_FRACTIONS in('NOX') -- gains_china-->15160

insert into gains_glob.emiss_tmp select r.m_reg, r.idregions, r.country, e.IDPOLLUTANT_FRACTIONS,

e.idyears, e.idact, e.idsec, e.activity, e.emiss from

gains_glob.MESSAGE_REGIONS_ALL r join gains_india.emiss_all_message e on r.idregions=e.idregions and r.gains_scen=e.idscenarios where r.gains_scheama='gains_india' and IDPOLLUTANT_FRACTIONS in('NOX') -- gains_india-->9849

insert into gains_glob.emiss_tmp select r.m_reg, r.idregions, r.country, e.IDPOLLUTANT_FRACTIONS,

e.idyears, e.idact, e.idsec, e.activity, e.emiss from

gains_glob.MESSAGE_REGIONS_ALL r join gains_world.emiss_all_message e on r.idregions=e.idregions and r.gains_scen=e.idscenarios where r.gains_scheama='gains_world' and IDPOLLUTANT_FRACTIONS in('NOX') -- gains_world-->16102

select e.idyears, r.m_reg, e.IDPOLLUTANT_FRACTIONS as pollutant, t1.sec_message as tec,

sum(e.ACTIVITY) as activity, sum(e.emiss) as emiss, sum(e.emiss)/sum(e.ACTIVITY) as ief

from gains_glob.emiss_tmp e join gains_glob.message_regions_all r on r.idregions=e.idregions

inner join gains_glob.trans_message_all t1 on t1.idact=e.idact and t1.idsec=e.idsec where e.IDPOLLUTANT_FRACTIONS in('NOX')

group by e.idyears, r.m_reg, e.IDPOLLUTANT_FRACTIONS, t1.sec_message order by e.IDPOLLUTANT_FRACTIONS, r.m_reg, t1.sec_message, e.idyears

select e.idyears, r.m_reg, e.IDPOLLUTANT_FRACTIONS as pollutant, t1.sec_message as tec, e.idsec, e.idact,

sum(e.ACTIVITY) as activity, sum(e.emiss) as emiss, sum(e.emiss)/sum(e.ACTIVITY) as ief

from gains_glob.emiss_tmp e join gains_glob.message_regions_all r on r.idregions=e.idregions

inner join gains_glob.trans_message_all t1 on t1.idact=e.idact and t1.idsec=e.idsec where e.IDPOLLUTANT_FRACTIONS in('NOX')

group by e.idyears, r.m_reg, e.IDPOLLUTANT_FRACTIONS, t1.sec_message, e.idsec, e.idact

order by e.IDPOLLUTANT_FRACTIONS, r.m_reg, t1.sec_message, e.idsec, e.idact, e.idyears

Appendix II

Characteristics of global GGI scenarios, derived from Riahi et al. (2007).

Scenario Description

B2

This scenario anticipates a world in which the emphasis is placed on local solutions to economic, social, and environmental sustainability. It is a world with continuously increasing population at a moderate rate, intermediate levels of economic development, and a diverse technological change. The B2 scenario is characterized by ‘dynamics as usual‘ rates of change, inspired by historical analogies where appropriate. World population growth is assumed to reach some 10 billion by 2100, assuming strong convergence in fertility levels toward replacement levels, ultimately yielding a stabilization of world population levels. The economic growth outlook in B2 is regionally more heterogeneous, with per capita income growth and convergence assumed to be intermediary between the two more extreme scenarios A2 and B1. Global economic output increases by a factor of 10 until 2100. Global carbon emissions rise initially along historical rates (to some 13 Gt by 2050), but growth would eventually slow down (14 Gt by 2100) as progressively more regions shift away from their reliance on fossil fuels, a twin result of technological progress in alternatives and increasing scarcity of easy-access fossil resources.

A2

The A2 storyline describes a very heterogeneous world with a slow convergence of fertility patterns across regions. The resulting ‘high population growth’ scenario adopted here is expects 12 billion by 2100. Economic development is primarily regionally oriented and per capita economic growth and technological change is more fragmented and slower than in other scenarios. In this scenario, per capita income growth is the lowest among the scenarios explored and converges only extremely slowly, both internationally and regionally. The more limited rates of technological change that result from the slower rates of both productivity and economic growth translates into lower improvements in resource

The A2 storyline describes a very heterogeneous world with a slow convergence of fertility patterns across regions. The resulting ‘high population growth’ scenario adopted here is expects 12 billion by 2100. Economic development is primarily regionally oriented and per capita economic growth and technological change is more fragmented and slower than in other scenarios. In this scenario, per capita income growth is the lowest among the scenarios explored and converges only extremely slowly, both internationally and regionally. The more limited rates of technological change that result from the slower rates of both productivity and economic growth translates into lower improvements in resource

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